A Restricted Latent Class Model with Polytomous Attributes and Respondent-Level Covariates
Abstract
We present an exploratory restricted latent class model where response data is for a single time point, polytomous, and differing across items, and where latent classes reflect a multi-attribute state where each attribute is ordinal. Our model extends previous work to allow for correlation of the attributes through a multivariate probit specification and to allow for respondent-specific covariates. We demonstrate that the model recovers parameters well in a variety of realistic scenarios, and apply the model to the analysis of a particular dataset designed to diagnose depression. The application demonstrates the utility of the model in identifying the latent structure of depression beyond single-factor approaches which have been used in the past.
Cite
@article{arxiv.2408.13143,
title = {A Restricted Latent Class Model with Polytomous Attributes and Respondent-Level Covariates},
author = {Eric Alan Wayman and Steven Andrew Culpepper and Jeff Douglas and Jesse Bowers},
journal= {arXiv preprint arXiv:2408.13143},
year = {2026}
}
Comments
42 pages, 1 figure, 11 tables. Added second simulation study, expanded explanations, added runtime information, and fixed typos. The version of record of this article, first published in Behaviormetrika, is available on the publisher's website at https://doi.org/10.1007/s41237-025-00271-8